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Evidence for large inversion polymorphisms in the human genome from HapMap data.

Vikas Bansal1, Ali Bashir, Vineet Bafna

  • 1Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093-0004, USA. vibansal@cs.ucsd.edu

Genome Research
|December 23, 2006
PubMed
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This study introduces a statistical method to detect large inversion polymorphisms in the human genome using linkage disequilibrium patterns. The approach identified 176 candidate inversions, offering new insights into human genetic variation.

Area of Science:

  • Human Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Structural variations, particularly inversions, are challenging to detect in the human genome.
  • Understanding these variations is crucial for comprehending human genetic diversity.

Purpose of the Study:

  • To develop and validate a statistical method for identifying large inversion polymorphisms.
  • To analyze inversion patterns in human populations using high-density SNP data.

Main Methods:

  • Utilized unusual Linkage Disequilibrium (LD) patterns from high-density SNP data.
  • Developed a statistical approach to detect population-level inversions relative to the reference genome.
  • Validated the method using simulations with HapMap data.

Related Experiment Videos

Main Results:

  • Identified 176 candidate inversions ranging from 200 kb to several megabases.
  • Detected specific inversions at 7p22, 16p12, and a novel one on chromosome 10.
  • Found 11 inversions flanked by homologous repeats and evidence for three in the Celera assembly.

Conclusions:

  • The statistical method effectively identifies potential large inversion polymorphisms.
  • Predicted inversions serve as strong candidates for further experimental validation.
  • This work advances the detection of complex structural variations in the human genome.